Method and device for analyzing substances contained in an area

ABSTRACT

An area to be analyzed is divided into a large number of small regions. Light is projected on each of the small regions, and data on the spectrums of the small regions are obtained. Plural principal components are extracted from the obtained data (step 208). The principal component marks of the respective plural components are computed for each of the small regions (step 212). It is determined whether the principal component scores of the respective small regions exceed a predetermined value or not, respectively. Then, the plural small regions are classified into a plurality of small region groups so that the small regions, whose principal component marks of specified principal components exceed a predetermined value, are included in an identical small region group (step 214). A representative small region (best point) of the representative principal component marks of the specified principal component is extracted from each of the small region groups (step 224). Based on the spectrum of the representative small region, the substances constituting the small region groups are analyzed.

TECHNICAL FIELD

The present invention relates to a method for analyzing an area and adevice for analyzing an area, to which the method for analyzing an areais applied.

BACKGROUND INVENTION

Generally, when an organic substance is analyzed to determine thecomposition of a functional group, infrared radiation is radiated ontothe substance. The infrared radiation which has penetrated through thesubstance or has been reflected from the substance is resolved intospectrums. The infrared intensity for each predetermined wavelength ismeasured to obtain the spectrum. The infrared wavelength, in which theintensity of absorbing the projected infrared radiation is at itsmaximum, is respectively different for each functional group. Therefore,the spectrum obtained by measuring the intensity of infrared radiation,which has penetrated through or has been reflected from the substance,generates a peak in its wave form (a maximum value or a minimum value),in accordance with the functional group constituting the organicsubstance. The wavelength of infrared radiation, in which the peak isgenerated, allows the functional group constituting the substance to bedetermined.

When synthetic resin parts such as plastics are checked for impuritiesand are found to contain impurities and the positions and constitutionsof the impurities are analyzed, a so-called area analysis is carried outin which the synthetic parts are cut in a plane, the plane is dividedinto a plurality of small regions, and infrared radiation is projectedonto the small regions so as to analyze each small region in a similarmanner to the above.

However, even if elements which constitute substances such as syntheticresin are identical to each other, the properties of the respectivesubstances are very different from each other due to a couplingcondition of the molecules or the like. Accordingly, when carrying outthe above-described analysis, the method in which a wave formrepresenting a spectrum changes, especially a wavelength of infraredradiation and an infrared intensity at a peak of a wave formrepresenting the spectrum has been observed and pattern matching hasbeen carried out with the state in which the wave form representing thespectrum of previously measured standard samples changes, in order tospecify the substances. Therefore, in order to minimize errors andobtain an accurate infrared radiation intensity, it is necessary tocarry out several measurements and then adopt the average value, so asto improve accuracy, thereby resulting in an increase in the timerequired for measurement, analysis, and the like.

With the aforementioned in view, a first object of the present inventionis to obtain an area analysis method capable of carrying out measurementand analysis in a short period of time.

A second object of the present invention is to obtain an area analysisdevice capable of carrying out measurement and analysis in a shortperiod of time.

DISCLOSURE OF THE INVENTION

In order to achieve the aforementioned objects, a first aspect of thepresent invention will now be explained. An area to be analyzed isdivided into a large number of small regions. These small regions aremeasured photometrically in order to obtain the spectrums of the smallregions. Plural principal components are extracted from the obtainedspectrums of the small regions, and the principal component marks ofeach of the extracted plural components are computed for each of thesmall regions. Then, the plural small regions are classified into pluralsmall region groups so that the small regions, whose principal componentmarks of specified principal components exceed a predetermined value,are included in an identical small region group, and the substancesconstituting the classified small region groups are analyzed.

In the first aspect of the present invention, preferably, an optimumsmall region of the optimum principal component marks of the specifiedprincipal component is extracted from each of the small region groups,and based on the spectrum of the optimum small region, the substancesconstituting each group are analyzed.

A second aspect of the present invention comprises a measuring meanswhich divides an area to be analyzed into a large number of smallregions and photometrically measures these small regions to obtain thespectrums of the small regions; a computing means which extracts pluralprincipal components from the obtained spectrums of the small regionsand computes the principal component marks of the respective pluralcomponents for each of the small regions; a classifying means whichclassifies the plural small regions into plural small region groups sothat the small regions, whose principal component marks of specifiedprincipal components exceed a predetermined value, are included in anidentical small region group; and an analyzing means which analyzes thesubstances constituting the classified small region groups.

The analyzing means of the second aspect of the present invention,preferably, extracts an optimum small region of the optimum principalcomponent marks of the specified principal component from each of thesmall region groups, and analyzes the substances constituting the smallregion groups based on the spectrum of the optimum small regions.

In the first aspect of the present invention, an area to be analyzed isdivided into a large number of small regions and these small regions aremeasured photometrically, to obtain spectrums, and plural principalcomponents are extracted from the obtained spectrums of the smallregions, and the principal component marks of each of the extractedplural components are computed for each of the small regions. Theprincipal components represent a portion in which data values are widelyscattered, i.e., in the present invention an important feature forspecifying the substances constituting the small regions. The spectrumis composed of data representing light intensity or the like for eachpredetermined wavelength. The principal components are expressed bycoefficients (eigenvector) which provide the respective weight to eachdata. A large weight is given to a wavelength in which data values arewidely scattered. When an area to be measured, comprising substance A inwhich a large peak is generated in the spectrum, e.g., at the wavelengthλ₁, and substance B in which no peak is generated in the spectrum, e.g.,at the wavelength λ₁, is divided into a plurality of small regions, soas to obtain the spectrums of the small regions, in comparison betweenthe spectrums of the small regions, data values at the wavelength λ₁ arewidely scattered. Thus, the value of data at the wavelength λ₁ in whichthe values are extensively scattered, is the important feature forspecifying the substances. The principal components extracted in theaforementioned case are expressed by means of coefficients such that aweighting of the data can be increased at the wavelength λ₁.Accordingly, the principal component marks of the respective principalcomponents, which are computed based on the coefficients, are greatlydifferent between one small region constituted by substance A andanother small region constituted by substance B. Therefore, it isdetermined whether the principal component scores of the respectivesmall regions exceed a predetermined value or not respectively, and whenthe plural small regions are so classified into plural small regiongroups that the small regions, whose principal component marks ofspecified principal components exceed a predetermined value, areincluded in an identical small region group, it can be determined thatthe small regions constituting the specified small region groups beconstituted by identical substances, and the substances constituting therespective small regions can be respectively specified by analyzing thesubstances constituting the small region groups.

Further, even if there are plural places in which data values are widelyscattered, respectively, it is possible to represent the plural placesof scattering by one principal component. Therefore, the feature of thespectrum of a synthetic resin product and the like, which is constitutedby a large number of peaks, can be represented by a small number ofprincipal components. In this manner, the principal components aredetermined in accordance with the extent of scattering of the data, andany high accuracy in the value of the respective data is not required.Even if, for example, values of light intensity to be measured aresomewhat scattered due to errors, a scattering of values due to pluralpeaks occurring at the specified wavelength is, i.e., in comparisonbetween the principal components, sufficiently small, thereby notcausing the analyzed results to be largely affected. Accordingly, it isnot necessary to obtain the spectrums plural times to improve precisionin measurement of light intensity at peaks or the like of the spectrums,thereby allowing the time required for measurement, analysis, and thelike, to be shortened.

In the first aspect of the present invention, an optimum small region ofthe optimum principal component marks of the specified principalcomponent is extracted from each of the small region groups, and theanalysis of the substances constituting each group is preferablyperformed based on the spectrum of the optimum small region. It can bejudged that the spectrum of which principal component scores ofspecified principal components are highest or the spectrum of a centralsmall region, remarkably have the feature that the specified principalcomponents exhibit. Therefore, it is possible to easily specify thesubstances by the above-described spectrums.

In the second aspect of the present invention, an area to be analyzed isdivided into a large number of small regions and these small regions arephotometrically measured in order to obtain the spectrums, and pluralprincipal components are extracted from the obtained spectrums of therespective small regions, and the principal component marks of therespective plural components are computed for each of the small regions.Thereby, the plural principal components in which a large weight isimposed on the wavelength in which data values are widely scattered, areextracted from the spectrums of the respective small regions. The smallregions which are respectively constituted by different substances aredifferent from each other in the principal component marks of specifiedprincipal components. Therefore, it is determined whether the principalcomponent scores of the respective small regions exceed a predeterminedvalue or not respectively, and when the plural small regions are soclassified into plural small region groups that the small regions, whoseprincipal component marks of specified principal components exceed apredetermined value, are included in an identical small region group, itcan be determined that the small region constituting the specified groupbe constituted by identical substances, and the substances constitutingthe respective small regions can be respectively specified by analyzingthe substances constituting the respective small region groups. Further,since the principal components are determined in accordance with theextent of scattering of data and any high accuracy in values of therespective data is not required, it is not necessary to obtain thespectrums a plurality of times to improve precision in measurement oflight intensity at peaks or like of the spectrums, thereby allowing thetime required for measurement, analysis, and the like, to be shortened.

In the second aspect of the present invention, the analyzing meanspreferably extracts a small region in which principal component marks ofthe specified principal component is highest or a central small regionfrom the respective small region groups and analyzes the substancesconstituting the small region groups based on the spectrum of the smallregion. It can be judged that the spectrum of which principal componentmarks of specified principal components are highest or the spectrum of acentral small region, remarkably have the feature that the specifiedprincipal components exhibit. Therefore, it is possible to easilyspecify the substances by the above-described spectrums.

With the above-described construction of the present invention, thespectrums can be obtained by projecting infrared radiation onto an areato be analyzed. Further, it is possible to obtain the spectrums byprojecting visible light onto an area to be analyzed by using Ramanscattering.

As explained above, the first and the second aspects of the presentinvention can obtain an excellent effect in that it is possible toperform measurement and analysis in a short time, since the area to beanalyzed is divided into a large number of small regions, and thesesmall regions are photometrically measured so as to obtain the spectrumsof the small regions. In addition, plural principal components areextracted from the spectrums of the small regions, and the principalcomponent marks of the respective plural components are computed foreach of the small regions. Then, the plural small regions are classifiedinto plural small region groups so that the small regions, whoseprincipal component marks of specified principal components exceed apredetermined value, are included in an identical small region group, sothat the substances constituting the small region groups may beanalyzed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an infrared area analysis deviceaccording to the present invention;

FIG. 2 is a flow chart illustrating a measuring process for each smallregion of the present invention;

FIG. 3 is a flow chart illustrating an area analysis process of thepresent invention;

FIG. 4 is a diagram illustrating the concept of small regions.

FIG. 5A is a schematic diagram showing an example of an image of an areato be analyzed;

FIG. 5B is a schematic diagram showing an example in which a classifiedresult is displayed;

FIG. 6 is a line diagram for illustrating a function of principalcomponent analysis; and

FIG. 7 is a line diagram showing an example in which a measured resultof the best point is displayed.

BEST EMBODIMENTS FOR CARRYING OUT THE PRESENT INVENTION

Referring to the attached drawings, the embodiment of the presentinvention will be described hereinafter. In FIG. 1, there is shown aninfrared area analysis device 10 according to the present embodiment.Rather than a wavelength, the infrared area analysis device 10 employs awave number (a reciprocal of a wavelength) as a fundamental unit. Theinfrared area analysis device 10 is provided with an infrared radiationgenerator 12 which emits infrared radiation having a predetermined wavenumber. The infrared radiation generator 12 is connected to a controlunit 16 via a control box 14, and emits infrared radiation in accordancewith instructions from the control unit 16.

Above the infrared radiation generator 12, an XY table 18 is disposed onwhich a sample to be analyzed SA is placed. The XY table 18 islight-transmissive, and the sample to be analyzed SA which is placed onthe XY table 18 is cut thin so as to facilitate transmission of infraredradiation. Thus, infrared radiation emitted from the infrared radiationgenerator 12 is transmitted through the XY table 18 and the sample to beanalyzed SA. The XY table 18 is coupled to the driving portion 20 and ismovable in the X direction and Y direction, i.e., two-dimensionally, bythe driving portion 20. The driving portion 20 is connected with thecontrol unit 16, and moves the XY table 18 in accordance with aninstruction from the control unit 16. Above the XY table 18, a lensbarrel 22 is provided in which a diaphragm, a polarizer, and the like(not shown) are accommodated. The infrared radiation which haspenetrated through the XY table 18 and the sample to be analyzed SA ismade incident within the lens barrel 22. The lens barrel 22 is connectedto the control unit 16 via the control box 14. The control unit 16controls an operation of the diaphragm and the like. A video camera 24with image-pickup elements such as CCDs is mounted on the infraredradiation output side of the lens barrel 22. The video camera 24, whichis connected to the control unit 16, receives the infrared radiationwhich has penetrated through the sample to be analyzed SA and has passedthrough the lens barrel 22, and outputs a video signal representing theimage of the sample to be analyzed SA to the control unit 16.

In addition, an infrared spectrophotometer 26 is mounted on the lensbarrel 22. The lens barrel 22 emits one portion of the incident infraredradiation to the infrared spectrophotometer 26. The infraredspectrophotometer 26 is provided with a spectroscope (not shown) whichspectrally resolves the infrared radiation incident from the lens barrel22, and a photometer (not shown) which measures the intensity of theresolved infrared radiation. The infrared spectrophotometer 26 isconnected to the control unit 16, and a band of wave-numbers to bemeasured and a step width of wave-numbers to be measured of the spectrumare indicated by the control unit 16. The band of wave-numbers to bemeasured indicates a range of wave-numbers of infrared radiation to bemeasured, and the step width of wave-numbers to be measured indicatesthat the infrared radiation is measured at particular degrees ofwave-number width within the band of wave-numbers to be measured. Theinfrared spectrophotometer 26 measures the infrared radiation intensityin accordance with the indicated band of wave-numbers to be measured andthe step width of wave-numbers To be measured, and outputs the measureddata which represent the spectrum to the control unit 16.

The control unit 16 is provided with a magnetic disk 28. The measuredresults inputted from the infrared spectrophotometer 26 are stored inthe magnetic disk 28. A display 32 is connected to the control unit 16via a video printer 30. The control unit 16 outputs a video signal andthe like, which are outputted from the video camera 24, to the display32 via the video printer 30. Thus, the display 32 represents the imageof the sample to be analyzed SA. Further, based on the inputted videosignal, the video printer 30 prints, if required, the image of thesample to be analyzed SA. In addition, the display 34 is also connectedto the control unit 16. The control unit 16 allows the display 34 torepresent information concerning the analyzed results and the like. Akeyboard 36 for inputting data and the like is also connected to thecontrol unit 16.

Next, the operation of the present embodiment will be described.Referring to the flow chart of FIG. 2, a measuring process of the sampleto be analyzed is first explained.

In a step 100, information of the band of wave-numbers to be measured,the step width of wave-numbers, and the like of the spectrum, beingpreviously stored in the magnetic disk 28 or the like, is read out. Inthe present embodiment, the band of wave-number to be measured is set tobe in the range from 1,000 to 2,000 cm⁻¹ (a wavelength of 5,000 to10,000 nm) in which there is a little influence of moisture, carbonicacid gas, and the like. The step width of wave-numbers is set to be,e.g., about 4 to 16 cm⁻¹. Further, the infrared area analysis device 10divides an analyzed area of the sample to be analyzed SA into, forexample, 10×10 small regions 38 as shown in FIG. 4, or 30×30 smallregions, or the like, in order to perform measurements for therespective small regions. Therefore, information about positions(coordinates) of the respective small regions, is also read out.

In a step 102, based on the information about the positions of the smallregions, the XY table 18 is moved so that infrared radiation isprojected onto a small region to be first measured. In a step 104,infrared radiation is emitted from the infrared radiation generator 12.The infrared radiation emitted from the infrared radiation generator 12penetrates through the XY table 18 and the small region which is firstmeasured of the sample to be analyzed SA, and one portion of theinfrared radiation is made incident into the infrared spectrophotometer26 and is spectrally resolved. The remaining portion thereof is madeincident into the video camera 24.

In a step 106, the band of wave-numbers to be measured and the stepwidth of wave-numbers of the spectrum are indicated to the infraredspectrophotometer 26 and simultaneously the infrared spectrophotometer26 starts measuring the infrared radiation. Thereby, the infraredspectrophotometer 26 starts measuring the infrared radiation of whichthe intensity is larger than that having, e.g., the wave number of 2,000cm⁻¹ at one end of the band of wave-numbers to be measured, and measuresthe intensity of the infrared radiation to the other end of the band ofwave-number to be measured up to, e.g., the wave number of 1,000 cm⁻¹for each step width of wave-number The measurement data is outputted tothe control unit 16. When the infrared spectrophotometer 26 measures theintensity of the infrared radiation to the other end of the band ofwave-number to be measured up to the wave number of 1,000 cm⁻¹,measuring the spectrum for one small region is completed. In the nextstep 108, the input measurement data is stored in the magnetic disk 28.

The next step 110 determines whether the measuring process for all ofthe small regions has been completed or not. If the decision at the step110 is no, in the step 102 the XY table 18 is driven so that infraredradiation may be projected onto the small region to be measured next.Then, in steps 104 through 110 a measuring process is performed in thesame manner as that aforementioned. In this way, the measuring processis performed on all of the small regions by projecting infraredradiation onto the respective small regions successively. When thedecision at step 110 is yes, the measuring process is completed.

In the above-described process, when the intensity of infrared radiationof the wave numbers of p kinds are measured for the respective n smallregions, the measurement data X₁₁, . . . , X_(pn), as shown in thefollowing table 1, are obtained. These measurement data are stored inthe magnetic disk 28. The respective small regions are, for conveniencesake, numbered as small region numbers 1, 2, 3 . . . n so that they maybe distinguished from each other. Further, the measurement data of therespective small region, e.g., the measurement data (X₁₁, X₂₁, X₃₁, . .. , X_(p1)) of the small region numbered as 1 represents the spectrum ofthis small region 1.

                  TABLE 1                                                         ______________________________________                                        SMALL                                                                         REGION         WAVE NUMBERS                                                   NUMBER         X.sub.1                                                                              X.sub.2  X.sub.3                                                                            . . . X.sub.p                             ______________________________________                                        1              X.sub.11                                                                             X.sub.21 X.sub.31   X.sub.p1                            2              X.sub.12                                                                             X.sub.22 X.sub.32   X.sub.p2                            3              X.sub.13                                                                             X.sub.23 X.sub.33   X.sub.p3                            4              X.sub.14                                                                             X.sub.24 X.sub.34   X.sub.p4                            :              :      :        :          :                                   :              :      :        :          :                                   :              :      :        :          :                                   n              X.sub.1n                                                                             X.sub.2n X.sub.3n   X.sub.pn                            ______________________________________                                    

Next, based on the measurement data obtained by the above-describedmeasuring process, a process for area analysis on the sample to beanalyzed SA, is explained with reference to the flow chart of FIG. 3. Instep 200, the measurement data as shown in Table 1, which have beenstored in the magnetic disk 28, are read in. Step 202 determines whetherthe number n of small regions Is equal to the number of samplings, p, ormore. When the decision at the step 202 is no, the process proceeds tostep 204.

When principal components are extracted by a principal componentextracting process as described below, from the measurement data whichhave been obtained by measuring the intensity of infrared radiationhaving the wave numbers of p kinds for the respective n small regions,when there is little data, i.e., n<p, it is not possible to obtain anysolution of an eigenvalue, and in step 204 the number of data isreduced. For example, peaks of values of measurement data for therespective small regions are obtained, and measurement data ofpredetermined wave numbers of which values of measurement data for allthe small regions do not come to a peak, i.e., photometric data of whichvalues are less than the peak, are eliminated. Since the principalcomponents extracted from the measurement data impose a large weight ona variable (wave number) in which the measurement data are extensivelyscattered, e.g., the wave number of which a value of measurement datacomes to a peak in one small region and of which a value of measurementdata is less than a peak in another small region, they are not greatlyinfluenced even if the data of a predetermined wave number less than apeak in all the small regions are eliminated.

In the next step 206, normalization for varying the values of therespective measurement data is carried out so that the average value ofthe measurement data is 0 and the value of distribution thereof is 1.Thereby, influences from the state of an area to be analyzed. e.g., thescattering of light transmission, are eliminated. Step 208 extractsprincipal components. The principal components are extracted by carryingout, e.g., a computation as described below.

When p variables (wave numbers in the present embodiment) are measuredfor the respective n fields (the respective small regions in the presentembodiment), a composite variable, z is considered which is expressed bythe following formula (1) with p variables, X₁, X₂, . . . , X_(p) isused. ##EQU1##

The distribution V(z) of the composite variable z is expressed in thefollowing formula (2). ##EQU2##

Extraction of the principal components corresponds to maximizing thedistribution of the composite variables. Thus, coefficients a₁₁, . . . ,a_(pp), and fixed values λ₁, . . . , λ_(p) are computed which representp principal components as shown in the following table 2. A descriptionabout the deriving process of the principal components will be omitted.

                                      TABLE 2                                     __________________________________________________________________________           COEFFICIENT                                                                            COEFFICIENT                                                                            COEFFICIENT COEFFICIENT                              VARIABLE                                                                             OF 1ST   OF 2ND   OF 3RD      OF pTH                                   (VARIATE)                                                                            PRIN. COMP.                                                                            PRIN. COMP.                                                                            PRIN.COMP.                                                                             . . .                                                                            PRIN. COMP.                              __________________________________________________________________________    X.sub.1                                                                              a.sub.11 a.sub.12 a.sub.13 . . .                                                                            a.sub.p1                                 X.sub.2                                                                              a.sub.21 a.sub.22 a.sub.23 . . .                                                                            a.sub.p2                                 X.sub.3                                                                              a.sub.31 a.sub.32 a.sub.33 . . .                                                                            a.sub.p3                                 :      :        :        :           :                                        :      :        :        :           :                                        :      :        :        :           :                                        X.sub.p                                                                              a.sub.p1 a.sub.p2 a.sub.p3    a.sub.pp                                 EIGEN- λ.sub.1                                                                         λ.sub.2                                                                         λ.sub.3                                                                         . . .                                                                            λ.sub.p                           VALUES                                                                        __________________________________________________________________________

The relationship between the respective eigenvalues is as follows:

    λ.sub.1 ≧λ.sub.2 ≧. . . ≧λ.sub.p ≧0                                                 (3)

The first principal component corresponding to the maximum eigenvalue λ₁is expressed by composite variables described below, in which elementsof eigenvectors (a₁₁, . . . , a_(pp)) are coefficients.

    z.sub.1 =a.sub.11 X.sub.1 +a.sub.21 X.sub.2 +. . . +a.sub.p1 X.sub.p(4)

In the same manner, the second and subsequent principal components arerespectively represented as follows. ##EQU3## As will be obviously seenfrom the above-described formulas (4) and (5), the eigenvectors (a₁₁, .. . , a_(pp)) are coefficients for weighting each of the compositevariables z₁, . . . , z_(p). When, for example, the sample to beanalyzed SA is constituted by three kinds of substances A, B, and Chaving respective spectrums as shown in FIG. 6, and the distribution ofmeasurement data of the respective variables, i.e., the respective wavenumbers, is maximum at the wave number X₈, the value of the eigenvectora₈₁ of the first principal component is increased. The measurement dataat the time the infrared radiation of wave number X8 is projected, isgiven a large weight. Therefore, the substances A and B which haverespective peaks of infrared radiation intensity at the wave number X8are greatly different from the substance C with no peak generated, inthe composite variable Z₁ of the first principal component, i.e., thevalue of principal component marks which will be described below. Due tothe difference in tills value, it is possible to easily classifysubstances.

Further, as described above, the principal components are dependent uponthe distribution of the measurement data at the respective wave numbers.High accuracy is not required for values of the measurement data of therespective small regions. Accordingly, it is not necessary to performplural measurements as in the conventional method for computing anaverage value to improve precision in measurement of the intensity ofinfrared radiation at a peak and the like. It is also possible toshorten the time required for analysis due to a reduced time formeasurement and reduced data volume.

Step 210 determines the number D of principal components, and the numberM of substances, which indicates the number of substances constitutingan area to be analyzed. The number D of principal components is a numberof principal components employed in a process such as classification inthe next step 212 and in subsequent steps. The principal components inwhich the eigenvalue λ can satisfy the following expression (6) areemployed. ##EQU4##

The above expression (6) is an empirical expression obtained fromexperiments carried out by the inventor of the present invention. Theprincipal components which do not satisfy the expression (6) haveeigenvalues λ that are lowered, and the ratio of principal components tothe entire distribution of an original variable, the so-calledcontributive rate, is low. Therefore, there is little influence on aprocess such as classification in the next step 212 and in subsequentsteps. Also, there is no possibility of a problem occurring even If theyare not employed as principal components in a process such asclassification. Further, since it is possible to analyze the same numberof substances as that of the employed principal components, the step 210establishes the same value as the number D of the principal component,assuming that the number of substances is M.

Step 212 computes the principal component marks of the respectiveprincipal components for each of the small regions. The principalcomponent marks are the values of the principal components z₁, . . .z_(p) which are computed by substituting measurement data X₁₁, X₂₁, X₃₁,. . . X_(p1) for variables X₁, X₂, . . . , X_(p) in the formulas (4) and(5). However, only the principal component marks of the principalcomponents which are employed in step 210 are computed for each of thesmall regions. For example, if the number of principal components Dequals three, the principal component marks z₁₁, . . . , z_(3n) of thefirst to the third principal components are computed for each of thesmall regions.

                  TABLE 3                                                         ______________________________________                                        SMALL                                                                         REGION   1ST PRIN.   2ND PRIN.   3RD PRIN.                                    NUMBER   COMPONENT   COMPONENT   COMPONENT                                    ______________________________________                                        1        Z.sub.11    Z.sub.21    Z.sub.31                                     2        Z.sub.12    Z.sub.22    Z.sub.32                                     3        Z.sub.13    Z.sub.23    Z.sub.33                                     :        :           :           :                                            :        :           :           :                                            n        Z.sub.1n    Z.sub.2n    Z.sub.3n                                     ______________________________________                                    

The principal component marks respectively represent the relationshipbetween each data (measurement data of the respective small region inthe present embodiment) and each principal component. For example, asmall region in which the principal component marks of the firstprincipal is highest or a central small region has remarkably thefeature represented by the first principal component, and it can bedetermined that the first principal component generates a peak at thewave number in which a large weight is given. Therefore, if classifiedbased on the magnitude of the principal component marks, it can bedetermined that the small regions classified into an identical smallregion group are constituted by identical substances.

Step 214 classifies the respective small region based on the principalcomponent marks. In this classification method, it is determinedwhether, for example, the principal component marks z₁₁, . . . , z_(1n)of the first principal component of the respective small regions exceeda reference score (e.g., 1) or not, and then the small region, whoseprincipal component marks exceed a reference score, is classified as asmall region which corresponds to the first principal component. Next,it is determined whether, the principal component marks z₂₁, . . . ,z_(2n) of the second principal component of the small regions which havenot been classified into the first principal component, exceed areference value or not. They are then classified in the same manner asabove. Further, it is determined whether, the principal component marksz₃₁, . . . , z_(3n) of the third principal component of the smallregions which have not been classified into the second principalcomponent, exceed a reference value or not. They are also thenclassified in the same manner as above. In case of the number ofprincipal components exceeding three, the small regions are alsoclassified in the same manner. In the present embodiment, an initialvalue of reference marks is one. When the classification is completed,the classified results are represented in the display 34, as shown inFIG. 5B. The classified results are the actual image of an area to beanalyzed, and are represented with the same color for each of theclassified small region groups. Unclassified small regions (for example,the region B of FIG. 5B) of which no principal component marks exceedsthe reference marks in the above-described classification process arerepresented with no coloring.

Step 216 determines whether the classified results are identical withthe entity or not. An operator of the infrared area analysis device 10makes a comparison between the image of an area to be analyzed of thesample to be analyzed SA represented in the display 32 as shown in FIG.5A, and the classified results represented in the display 34 as shown inFIG. 5B and determines whether or not, for example, the classifiedresults correspond to the actual color distribution of the area to beanalyzed, and then inputs the determined results by operating a keyboard 36. For example, assuming that it is determined that, in FIG. 5B,a small region 40 and a small region 42 do not correspond to the realobject, in such a case the determined results that they do notcorrespond to the entity, are inputted. If the determined results thatthey do not correspond to the entity are inputted, the process proceedsto step 218 where it is determined whether any unclassified smallregions which are represented with no coloring exist or not, if thedecision in step 218 is yes, step 220 subtracts 0.1 from the referencemarks, and the process is returned to step 214. Thus, classificationcriteria is lowered, so as to carry out re-classification. If thedecision at step 218 is no, the number D of the principal components andthe values of the number M of the substances are corrected so as tocorrespond to the entity. The process then returns to step 212. Then,the processes in steps 212 through 222 are repeated until the decisionat the step 216 is yes.

If the decision at step 216 is yes, the process proceeds to step 224,and the best point which is the small region of the highest principalcomponent marks among the respective small region groups classified thatare constituted by an identical substance, is extracted from each of thesmall region groups. In step 226, in the same way as the measuringprocess of the flow chart in FIG. 2, infrared radiation is projectedonto the respective small regions which have been extracted as the bestpoint from each of the small region groups. The intensity of infraredradiation is measured by varying the wave numbers of infrared radiation.This allows the spectrums for the respective best points to be obtainedas shown in FIG. 7, and the spectrums are represented in the display 34.

Next, step 228 determines whether estimation (identification) of thesubstances constituting the respective best points is executed or not inthe infrared area analysis device 10. It requires skill in estimatingsubstances with reference to the displayed spectrums. If an operator ofthe infrared area analysis device 10 is a skilled worker, the decisionat step 228 will be yes. If the operator is unskilled in the estimationof substances, the decision at step 228 will be no. If the decision atstep 228 is yes, the process proceeds to step 230, and a similarspectrum is retrieved from the previously-stored spectrums. In thiscase, since the best point is the small region of the highest principalcomponent marks among each of the small region groups, it is the smallregion which has the remarkable feature represented by the respectiveprincipal components. Accordingly, when the respective principalcomponents represent the feature of substances accurately, it ispossible to easily estimate the substances of each best point.

Step 232 determines whether the spectrums of the respective best pointsmeasured in step 226 are those of existing substances. If, for example,a similar spectrum does not exist in the process in step 230, or if thesubstances have not been judged what they are when the substances areestimated by a skilled worker, this decision is made no. When thedecision in step 232 is no, it is considered that the principalcomponents do not accurately represent the features of substancesconstituting the respective small regions, for example, the small regionof the best point is constituted by a plurality of substances.Therefore, the values of the number M of substances are corrected instep 234, and the process is returned to step 212. If the decision atstep 232 is yes, the process is completed.

In this manner, in the present embodiment, plural principal componentsof which the respective wavelengths are expressed as variables areextracted, the principal component marks of the plural principalcomponents are computed for each of the small regions, and based on theprincipal component marks the small regions are respectively classified.Therefore, it is not necessary to perform measurements a plurality oftimes. It is also unnecessary to improve the precision of measurement ofthe intensity of infrared radiation at a peak or the like. Further, itis possible to reduce measuring time, and analysis time due to adecreased amount of data.

Although, in the present embodiment, spectrums are obtained by measuringthe intensity of infrared radiation which penetrates through the sampleto be analyzed SA, it is also possible to obtain the spectrums bymeasuring the intensity of infrared radiation reflected from the sampleto be analyzed SA. Further, the spectrums can be also obtained byprojecting visible light on an area to be analyzed and then using Ramanscattering.

In addition, although the above-described embodiment is given as anexample in which an area to be measured is divided into a large numberof small regions and measured photometrically by moving the sample to beanalyzed, the present invention is not limited to this example. The areato be measured may be divided into a large number of small regions andmay be measured photometrically so as not to move the sample to beanalyzed by scanning infrared radiation on the area to be measured bymeans of a rotating polygon mirror, a galvanometer mirror, and the like.

Further, although the above-described embodiment is given as an examplein which a small region of the highest principal component marks or asmall region in the center is extracted and analyzed, values of theprincipal component marks of the small region to be extracted can bedetermined through experiments, and based on the principal componentmarks, the small region most suitable for analysis of substances can beextracted and analyzed.

I claim:
 1. An area analysis method, comprising the steps of:dividing anarea to be analyzed into a plurality of small regions and measuring thesmall regions photometrically to obtain spectrums of the respectivesmall regions; extracting a plurality of principal components from saidobtained spectrums of the respective small regions; computing principalcomponent marks of each of said extracted principal components for eachof the small regions; classifying the plurality of small regions into aplurality of small region groups so that the small regions whoseprincipal component marks of specified principal components exceed apredetermined value are included in identical ones of the small regiongroups; and determining the substances constituting the classified smallregion groups based on the principal component marks.
 2. An areaanalysis method according to claim 1, further comprising the step ofextracting an optimum principal component mark of said specifiedprincipal component from each of said small region groups, wherein thestep of determining the substances constituting the small region groupsis carried out based on the optimum principal component mark.
 3. An areaanalysis method according to claim 1, wherein an eigenvalue applied as aweighting factor to the principal components satisfies the followingexpression: ##EQU5## wherein, m=1, . . . , p, and p is a number of datawhich are measured in the respective small regions.
 4. An area analysisdevice, comprising:photometric measuring means for dividing an area tobe analyzed into a plurality of small regions and photometricallymeasuring the small regions to obtain spectrums of the respective smallregions; computing means for extracting a plurality of principalcomponents from said obtained spectrums of the respective small regionsand computing principal component marks of each of said extractedprincipal components for each of the small regions; classifying meansfor classifying the large number of small regions into a plurality ofsmall region groups so that the small regions whose principal componentmarks of specified principal components exceed a predetermined value areincluded in identical ones of the small region groups; and determiningmeans for determining the substances constituting the classified smallregion groups based on the principal component marks.
 5. An areaanalysis device according to claim 4, wherein said analyzing meansextracts a representative principal component mark of said specifiedprincipal component from each of said small region groups, anddetermines the substances constituting the small region groups based onthe representative principal component mark.
 6. An area analysis device,according to claim 4, wherein an eigenvalue applied as a weightingfactor to the principal components satisfies the following expression:##EQU6## wherein, m=1, . . . , p, and p is a number of data which aremeasured in the respective small regions.